Through an AI's Looking Glass: Discovering Dental Sexual Dimorphism with Explainable AI
Issued Date
2025-01-01
Resource Type
Scopus ID
2-s2.0-105032534850
Journal Title
Proceedings 2025 International Conference on Digital Image Computing Techniques and Applications Dicta 2025
Rights Holder(s)
SCOPUS
Bibliographic Citation
Proceedings 2025 International Conference on Digital Image Computing Techniques and Applications Dicta 2025 (2025)
Suggested Citation
Hirunchavarod N., Sributsayakarn N., Pornprasertsuk-Damrongsri S., Jirarattanasopha V., Intharah T. Through an AI's Looking Glass: Discovering Dental Sexual Dimorphism with Explainable AI. Proceedings 2025 International Conference on Digital Image Computing Techniques and Applications Dicta 2025 (2025). doi:10.1109/DICTA68720.2025.11302458 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/115791
Title
Through an AI's Looking Glass: Discovering Dental Sexual Dimorphism with Explainable AI
Corresponding Author(s)
Other Contributor(s)
Abstract
We propose a framework that extends explainable AI (XAI) from per-instance interpretation to dataset-wide knowledge discovery in dental morphology. A deep convolutional neural network was trained on 5,132 panoramic radiographs from 2,778 individuals to predict sex. Using OPG-SHAP, a domain-specific XAI method, we identified influential oral parts and validated them with statistical analysis. The upper canine was the most influential region, with females showing a significantly higher width-to-height ratio (0.391) than males (0.347), aligning with existing literature. Additionally, the upper third molar emerged as a novel sexually dimorphic feature, with males showing a higher ratio (1.064) than females (1.036). Both differences were statistically significant (p<0.001). Our results demonstrate how interpretable AI can rediscover known anatomical patterns and reveal new insights, enabling clinically meaningful knowledge extraction from neural networks.